A Machine Learning Framework for Prevention of Software-Defined Networking controller from DDoS Attacks and dimensionality reduction of big data

Abstract

The controller is an indispensable entity in software-defined networking (SDN), as it maintains a global view of the underlying network. However, if the controller fails to respond to the network due to a distributed denial of service (DDoS) attacks. Then, the attacker takes charge of the whole network via launching a spoof controller and can also modify the flow tables. Hence, faster, and accurate detection of DDoS attacks against the controller will make the SDN reliable and secure. Moreover, the Internet traffic is drastically increasing due to unprecedented growth of connected devices. Consequently, the processing of large number of requests cause a performance bottleneck regarding SDN controller. In this paper, we propose a hierarchical control plane SDN architecture for multi-domain communication that uses a statistical method called principal component analysis (PCA) to reduce the dimensionality of the big data traffic and the support vector machine (SVM) classifier is employed to detect a DDoS attack. SVM has high accuracy and less false positive rate while the PCA filters attribute drastically. Consequently, the performance of classification and accuracy is improved while the false positive rate is reduced.